Update app.py

app.py

@@ -30,21 +30,13 @@ def generate_predictions(medication_data, site, bmd, mu, sigma):
     site_data = medication_data[medication_data['Site'] == site]
     all_results = []
     
-    if site_data.empty:
-        st.warning(f"No data available for the selected site: {site}")
-        return all_results  # Return empty results
-
     for _, row in site_data.iterrows():
         drug = row['Medication']
         predictions = {'Year': ['0'], 'Predicted BMD': [round(bmd, 3)], 'Predicted T-score': [round(calculate_tscore(bmd, mu, sigma), 1)]}
         
-        st.write(f"Processing drug: {drug}")
-        
         for year in row.index[1:-1]:  # Skip 'Medication' and 'Site' columns
             if not pd.isna(row[year]):
                 percentage_increase = row[year]
-                st.write(f"Year: {year}, Percentage Increase: {percentage_increase}")
-                
                 predicted_bmd = bmd * (1 + percentage_increase)
                 predicted_tscore = calculate_tscore(predicted_bmd, mu, sigma)
                 
@@ -118,20 +110,13 @@ def main():
     
     # Load constants and medication data
     medication_data = load_medication_data()
-    st.write("Loaded Medication Data:")
-    st.write(medication_data.head())  # Display the loaded data for verification
-    
     constants = REGRESSION_CONSTANTS.get(selected_site, {})
-    if not constants:
-        st.warning(f"No constants available for the selected site: {selected_site}")
-        return
 
     # Generate and display predictions
     if st.button("Predict"):
-        st.write("Prediction started...")
         predictions = generate_predictions(medication_data, site, bmd_patient, constants['mu'], constants['sigma'])
         display_results(predictions, selected_site)
 
-
 if __name__ == "__main__":
     main()
+